Exogenous spatial attention Evidence for

Journal of Vision (2013) 13(14):9, 1–13

http://www.journalofvision.org/content/13/14/9

1

Exogenous spatial attention: Evidence for intact functioning in
adults with autism spectrum disorder
Department of Psychology, New York University,
New York, NY, USA
Center for Neural Science, New York University,
New York, NY, USA

$

Marlene Behrmann

Department of Psychology, Carnegie Mellon University,
Pittsburgh, PA, USA

$

Ryan Egan

Department of Psychology, Carnegie Mellon University,
Pittsburgh, PA, USA

$

Nancy J. Minshew

Departments of Psychiatry and Neurology, University of
Pittsburgh School of Medicine, Pittsburgh, PA, USA

$

David J. Heeger

Department of Psychology, New York University,
New York, NY, USA
Center for Neural Science, New York University,

New York, NY, USA

$

Marisa Carrasco

Department of Psychology, New York University,
New York, NY, USA
Center for Neural Science, New York University,
New York, NY, USA

$

Michael A. Grubb

Deficits or atypicalities in attention have been reported
in individuals with autism spectrum disorder (ASD), yet
no consensus on the nature of these deficits has
emerged. We conducted three experiments that paired a
peripheral precue with a covert discrimination task,

using protocols for which the effects of covert exogenous
spatial attention on early vision have been well
established in typically developing populations.
Experiment 1 assessed changes in contrast sensitivity,
using orientation discrimination of a contrast-defined
grating; Experiment 2 evaluated the reduction of
crowding in the visual periphery, using discrimination of
a letter-like figure with flanking stimuli at variable
distances; and Experiment 3 assessed improvements in
visual search, using discrimination of the same letter-like
figure with a variable number of distractor elements. In
all three experiments, we found that exogenous
attention modulated visual discriminability in a group of
high-functioning adults with ASD and that it did so in the

same way and to the same extent as in a matched
control group. We found no evidence to support the
hypothesis that deficits in exogenous spatial attention
underlie the emergence of core ASD symptomatology.

Introduction
Autism spectrum disorder (ASD) is characterized by
social communication deficits and restricted interests
and behaviors (American Psychiatric Association,
2013). Since the earliest description of the disorder
(Kanner, 1943), deficits or atypicalities in attention
have been noted, and it has been proposed that
attentional deficits may subserve the emergence of core
ASD symptomatology (Fan, 2012; Keehn, Muller, &
Townsend, 2013). An attention-based theory of ASD is

Citation: Grubb, M. A., Behrmann, M., Egan, R., Minshew, N. J., Heeger, D. J., & Carrasco, M. (2013). Exogenous spatial
attention: Evidence for intact functioning in adults with autism spectrum disorder. Journal of Vision, 13(14):9, 1–13, http://www.
journalofvision.org/content/13/14/9, doi:10.1167/13.14.9.
doi: 10 .116 7 /1 3. 14 . 9

Received July 19, 2013; published December 10, 2013

ISSN 1534-7362 Ó 2013 ARVO

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

alluring because it might be able to account for two of
the primary symptoms of ASD, social and communication deficits and restricted interest and behaviors, if
attentional resources are diverted to support a
restricted behavioral repertoire rather than subserve the
acquisition of expertise in social communication.
However, attention has not been consistently defined in
the ASD literature (Ames & Fletcher-Watson, 2010),
and most of the empirical work done to date on
ostensible attention deficits in ASD has failed to take
advantage of the rigorous experimental protocols for
manipulating and measuring attention that are now
well established in the field of attention research.
When there is a sudden onset in the visual periphery,
the processing of visual information at that spatial
location is automatically facilitated. This involuntary
allocation of processing resources, known as covert

exogenous spatial attention, reaches its peak at ;100
ms post stimulus onset (Muller & Rabbitt, 1989;
Nakayama & Mackeben, 1989), faster than the time
needed to make an eye movement or to voluntarily
allocate spatial attention. Exogenous attention markedly impacts peripheral vision by increasing contrast
sensitivity, enhancing spatial resolution, increasing
processing speed, and modulating subjective appearance at the attended location (Cameron, Tai, &
Carrasco, 2002; Carrasco, Ling, & Read, 2004;
Carrasco & McElree, 2001; Herrmann, MontaserKouhsari, Carrasco, & Heeger, 2010; Hopfinger &
Mangun, 1998; Liu, Pestilli, & Carrasco, 2005;
Yeshurun & Carrasco, 1998).
Previous work on exogenous attention in ASD has
produced inconsistent findings, perhaps because of
methodological differences between the studies (see
Discussion), and no consensus on the integrity of
exogenous spatial attention in ASD has emerged. Any
attentional theory of ASD must account for exogenous
attention, which allows us to automatically respond to
environmental demands and to react quickly to stimuli
that provide behaviorally relevant information. Exogenous attention modulates both behavioral/psychophysical performance and neural activity in the visual

cortex (for recent reviews, see Anton-Erxleben &
Carrasco, 2013; Carrasco, 2011).
We conducted three experiments to assess the
functional integrity of this system, using visual tasks for
which the effects of exogenous spatial attention on
early vision have been well established in typically
developing populations: orientation discrimination of a
contrast-defined grating (which is monotonically contingent on contrast sensitivity, e.g., Carrasco, PenpeciTalgar, & Eckstein, 2000; Herrmann et al., 2010;
Nachmias, 1967; Pestilli, Ling, & Carrasco, 2009),
target discrimination with flanking stimuli at variable
distances (to evaluate crowding, e.g., Yeshurun &
Rashal, 2010), and target discrimination with a variable

2

number of distractor stimuli (to assess visual search,
e.g., Carrasco & McElree, 2001; Carrasco & Yeshurun,
1998). In all three experiments, we found that
exogenous attention modulated visual discriminability
in a group of high-functioning adults with ASD and

that it did so in the same way and to a statistically
indistinguishable degree as in a matched control group.
The results from these three experiments provide strong
evidence for intact exogenous orienting in highfunctioning adults with ASD.

Materials and methods
Observers and psychophysical sessions
Fourteen high-functioning adults with ASD (19–41
years, two female) and 16 typically developing control
individuals (19–47 years, three female) participated in
one or more of the three exogenous attention experiments. Participants were recruited by the Center For
Excellence in Autism Research (CeFAR) at the
University of Pittsburgh. All participants had Full
Scale and Verbal IQ scores above 79. All ASD
participants had been diagnosed using DSM-IV criteria, and all except one met criteria for autism on both
the autism diagnostic observation schedule (ADOS)
and the autism diagnostic interview (ADI); one met
criteria for autism on ADI but spectrum on ADOS.
Experimental procedures were approved by the institutional review boards at The University of Pittsburgh
and Carnegie Mellon University and by the University

Committee on Activities Involving Human Subjects at
NYU. Within each experiment, the participants were
matched at a group level on IQ (Wechsler, 1999),
gender, age, and education (Table 1). For all three
experiments, there were no statistically significant
group differences for Full Scale IQ, Verbal IQ,
Performance IQ, or age ( p values . 0.19, t tests).
Participants had normal or corrected-to-normal vision,
and none of the participants had been diagnosed with
ADHD. Experiment 1 (contrast sensitivity, 600 trials)
was completed during a single testing session, which
lasted approximately 1 hr. Experiments 2 and 3
(crowding and visual search, 960 and 384 trials,
respectively) were completed on a different day during
a single testing session but separated by a short break.
The order of the experiments was counterbalanced
across participants, and the total session time was
approximately 1.5 hr. Participants completed practice
blocks before each experiment (Experiment 1: three
blocks of 20 trials each; Experiment 2: four blocks of 24

trials each; Experiment 3: four blocks of 20 trials each).
In addition, for Experiment 1, each participant

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

ASD group

ADOS

3

ADI

Control group

Exp Age Verbal IQ Full IQ Sex COM SOC COM SOC STB SOC COM verbal STB

Exp Age Verbal IQ Full IQ Sex

1
1
1
1
1
3
1–3
1–3
1–3
1–3
1–3
2, 3
2, 3
2, 3

1
1
1
1
1
1
1
1
3
1–3
1–3
2, 3
2, 3
2, 3
2, 3
2, 3
2, 3

20
21
25
20
29
19
22
22
31
20
41
21
23
41

79
112
125
118
114
99
120
121
119
106
108
110
92
121

88
128
134
124
117
99
123
117
123
107
124
107
84
129

m
m
m
m
m
m
m
m
f
m
m
f
m
m

6
4
5
5
4
5
4
5
2
5
3
7
4
6

13
8
13
8
7
9
6
6
7
11
8
7
10
6

19
12
18
13
11
14
10
11
9
16
11
14
14
12

1
2
3
1
2
0
1
6
4
3
2
2
2
1

23
21
20
27
25
22
21
19
10
20
21
27
23
32

13
17
13
22
9
17
18
11
8
15
16
20
18
22

4
6
3
5
8
8
8
4
6
3
8
6
7
10

19
24
35
27
22
25
20
40
47
22
23
24
22
28
27
18
36

121
127
114
119
120
109
106
109
115
114
111
109
116
109
112
106
120

120
127
118
118
125
110
108
116
111
119
109
115
115
116
117
104
121

m
f
m
m
m
m
m
m
m
m
m
m
m
f
f
m
m

Table 1. Demographic and diagnostic information. Notes: ADOS, autism diagnostic observation schedule; COM, communication; SOC,
social interaction; COM SOC, communication þ social interaction; STB, stereotyped behaviors; ADI, autism diagnostic interview.

completed an additional block of trials to measure
orientation-discrimination thresholds.

Psychophysical task
In Experiment 1, participants performed a twoalternative forced-choice orientation-discrimination
task while maintaining fixation at the center of the
screen (Figure 1A, B). Spatial attention was manipulated via the presentation of a peripheral precue (see
below); the stimuli were sinusoidally modulated,
contrast-defined grating patches (Gabors); and the
target was indicated after the presentation of the
gratings with a postcue. On each trial, after the
presentation of the peripheral cue, four gratings were
presented for 30 ms, simultaneously, in each of the four
visual quadrants. Gratings were 38 of visual angle in
diameter (Gaussian window SD ¼ 0.438), 2 c/8, 60%
contrast, centered at 58 eccentricity in the middle of
each quadrant, and with the same mean luminance as
the uniform gray background. Each grating was tilted
slightly with respect to vertical. The direction of tilt,
clockwise or counterclockwise, was randomized on
each trial independently for each of the four gratings. A
response cue near fixation (0.858 green line) appeared
200 ms after the gratings disappeared and indicated
which of the four gratings was the target. Targets
always appeared in the lower visual quadrants, and
participants were told so explicitly at the beginning of
the experiment; the two upper gratings were shown so
that the stimulus display would match that of another

experiment not reported here. Participants indicated
whether the target was tilted clockwise or counterclockwise of vertical by pressing one of two buttons.
After the practice blocks, the amount of tilt was
determined in pretests to equate task difficulty separately for each participant. In the pretest (200 trials,
neutral cues only), a staircase procedure was used to
determine the degree of tilt necessary for a performance
accuracy of ;80% correct.
In Experiments 2 and 3, participants performed a
four-alternative forced-choice orientation-discrimination task while maintaining fixation at the center of the
screen (Figure 1C, D). Spatial attention was manipulated via the presentation of a peripheral precue (see
below), and the stimuli were figures comprised of two
bisecting lines. For target stimuli, the two lines bisected
at the top, right, bottom, or left of an imaginary box
(e.g., ‘‘T’’). Distractor stimuli were made of two lines
joined in the center by a third line (e.g., ‘‘H’’). Targets
and distractors measured 0.98 · 0.98 in size and were
presented simultaneously for 100 ms at 20% contrast
against a uniform gray background. The target and the
distractor elements randomly and independently appeared in one of four possible orientations. Participants
indicated whether the targets’ two lines bisected at the
top, right, bottom, or left of the center line by pressing
one of four buttons. In Experiment 2—crowding—the
target always appeared along the horizontal meridian
at 98 eccentricity and was flanked vertically by two
distractors with a center-center distance that varied
randomly from trial to trial (18, 1.58, 28, 2.58, 38, 48, 58,
78, 98, or 118). In Experiment 3—visual search—the

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Grubb et al.

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Figure 1. Experimental protocol. (A) Trial sequence, Experiment 1. ISI, interstimulus interval. Hash marks indicate extended temporal
delays, not shown to scale. (B) Cue types and example stimulus, Experiment 1. (C) Trial sequence, Experiments 2 and 3. Same format
as panel A. (D) Cue types and example stimuli, Experiments 2 and 3.

angular position of the target was randomly selected on
each trial, and the target appeared along an imaginary
98 ring around fixation. The number of distractor
elements (4, 9, 14, or 19) varied randomly from trial to
trial, and all elements were equally spaced along this
imaginary 98 ring, leading to a total set size of 5, 10, 15,
or 20 elements.
Auditory feedback (different tones for correct and
incorrect) was provided after each response in all three
experiments. In Experiment 1, participants were
required to respond within a 1000-ms window, and the
next trial began at the end of this window. A temporal
delay and a response tone were used in Experiments 2
and 3 to minimize differences in reaction times (RTs)
across experimental conditions and across participants,
thereby isolating the effects of attention on performance accuracy rather than on RT and preventing any
speed-accuracy trade-offs that might occur differentially between the groups. After the offset of the
stimulus, participants were required to wait 900 ms
before responding. A tone signaled the end of this

period and indicated to participants that they could
respond. Participants had up to 5000 ms to make a
response, and the next trial began immediately after a
response was made or this upper limit was reached.

Exogenous attention manipulation
A peripheral precue (Experiment 1: 60 ms; Experiments 2 and 3: 50 ms) was used to manipulate
exogenous spatial attention (Figure 1). In Experiment
1, there were three different trial types with two
different precues: valid and invalid (a white dot, 0.48 in
diameter, centered along the diagonal at 7.58 eccentricity) and neutral (two small white lines, 0.58, pointing
to the two lower locations from fixation). For valid
trials, the precue appeared in the lower quadrant in
which the target would appear, 2.58 away; for invalid
trials, it appeared in the lower quadrant opposite to
which the target would appear. A random third of the
trials had valid cues, another third had invalid cues,

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

and the remaining third had neutral cues. Thus, the
cues were always uninformative about the upcoming
target location, and participants were informed of this.
In Experiments 2 and 3, there were two trial types
that utilized the same precue (a white dot, 0.48 in
diameter). For valid trials, the precue appeared near the
subsequent target’s location at 88 eccentricity (18 closer
to fixation); for neutral trials, it appeared at fixation. A
random half of the trials had valid cues, and the
remaining half had neutral cues. The precue was
followed by a brief interstimulus interval to maximize
the effects of exogenous attention (Experiment 1: 40
ms; Experiments 2 and 3: 70 ms; the stimulus-onsetasynchrony (SOA) was 100 and 120 ms, respectively).
The peripheral cue in Experiments 2 and 3 was always
valid and, as a result, did provide information about
the spatial location of the upcoming target. It is well
established in the literature, however, that ;300 ms are
needed to voluntarily allocate endogenous spatial
attention (e.g., Liu, Stevens, & Carrasco, 2007; Muller
& Rabbitt, 1989; Nakayama & Mackeben, 1989; Rolfs
& Carrasco, 2012); therefore, participants would not
have had time to utilize this information. Furthermore,
it has also been established that cue validity does not
modulate the effect of exogenous attention with the
time intervals used here (Giordano, McElree, &
Carrasco, 2009).

Reaction time
RT distributions for correct trials only were computed separately for each participant and separately for
each experimental condition in all three experiments.
The median RT for correct responses was then
determined for each distribution.

Model fits
In Experiment 2, for each participant and attention
condition, we modeled the effect of target-flanker
distance on accuracy with the following exponential
function (following Scolari, Kohnen, Barton, & Awh,
2007; Yeshurun & Rashal, 2010):


ð1Þ
pc ¼ a 1  eðsðdiÞÞ
where pc ¼ proportion correct, a ¼ asymptote, s ¼
scaling factor, d ¼ target-flanker distance, and i ¼ x –
intercept. Parameters were estimated using nonlinear
least-squares fitting (MATLAB, The MathWorks, Inc.,
Natick, MA), using the default settings in MATLAB
for the iteration number and initializing the parameter
estimates with values reported by Yeshurun and Rashal
(2010). The critical distance (cd) was defined as the

5

target-flanker distance at which performance achieved
90% of the asymptotic value and was given by the
following equation:


ð2Þ
cd ¼ i  lnð0:1Þ=s
In Experiment 3, linear regression was use to fit
straight lines to the effect of set size on accuracy. Slopes
and y-intercepts were estimated separately for each
participant and attention condition.

Goodness of model fits
In both Experiments 2 and 3, the coefficient of
determination (R2) was used to assess how well each
model fit the data. R2 was calculated for each of the
exponential fits in Experiment 2 and each of the line fits
in Experiment 3 separately for each participant and for
each attention condition. Differences in R2 values
between groups were assessed using randomization
tests (see below).

Visual search and crowding correlation
The deleterious effect of adding distractors in visual
search (Experiment 3) could be due to crowding.
Accordingly, we estimated how well the crowding data
from Experiment 2 could predict performance in
Experiment 3. In Experiment 3, each set size (5, 10, 15,
and 20) was associated with a particular distance
between the target and the nearest distractor (10.588,
5.568, 3.748, and 2.828, respectively). For each participant and attention condition, we predicted accuracy at
each of these distances using the exponential fit derived
in Experiment 2. The correlation between performance
in visual search and estimated performance in crowding
was computed across these four target-flanker distances
separately for each participant and separately for each
attention condition. The resulting correlation values
were then Fisher-transformed, and a t test was used to
determine if the correlation values were significantly
greater than zero separately for each group and
separately for each attention condition. An ANOVA
was then used to compare the magnitude of the
correlation values within attention conditions and
between groups.

Eye tracking
Fixation was monitored throughout each experiment, using an infrared-video eye tracker (Eyelink, SR
Research, Ottawa, Ontario; 500 Hz sampling rate). A
nine-point calibration routine was completed at the

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

start of each experimental block. Trials in which a
participant’s gaze deviated from fixation by more than
28 of visual angle during the presentation of the
stimulus were not analyzed. Mean proportion excluded
in Experiment 1, 0.11 (ASD: 0.15, control: 0.06); in
Experiment 2, 0.071 (ASD: 0.10, control: 0.04); and in
Experiment 3, 0.052 (ASD: 0.057, control: 0.058). For
each experiment, we analyzed the proportion of
fixation breaks with a two-way mixed-model ANOVA
with attention condition (Experiment 1: valid, neutral,
invalid; Experiment 2–3: valid, neutral) as a withinsubject variable and group (ASD, control) as a
between-subjects variable. There were no attention
condition · group interactions in any of these
experiments, providing no evidence that the distribution of fixation breaks across the attention conditions
differed between groups.

Excluded participants
Some participants were excluded because of excessive fixation breaks. To assess eye movements, we
computed a median-based z-score value (i.e., substituting the median for the mean in the z-score
computation) for each participant’s proportion of
fixation breaks separately for each experiment. We
excluded from all subsequent analyses any participant
who had a z-score greater than two in that experiment.
Two participants from the ASD group and one from
the control group were excluded in Experiment 1. One
ASD participant and two controls were excluded in
Experiment 2. One participant from each group was
excluded in Experiment 3.
In addition, three ASD participants and one control
participant were excluded from Experiment 1 for being
unable to perform the task above chance levels in the
neutral condition. One participant was excluded from
the ASD group in Experiment 2 for not reaching
asymptotic performance in the neutral attention
condition, resulting in a critical distance outside the
tested range. Excluded participants are not included in
Table 1.

Randomization tests
Because R2 values are not normally distributed,
nonparametric randomization tests were used to assess
differences in model fits in Experiments 2 and 3.
Participants’ group labels were randomly shuffled, the
mean R2 value was recomputed for each group, and the
difference was recorded. This procedure was repeated
1,000 times to obtain a null distribution of betweengroup differences in R2 values. The p value reported is
the proportion of null distribution values greater than

6

or equal to the actual difference in R2 value; absolute
values were used in the computation to make this a
two-tailed test.

Ruling out possible stimulus confounds
We tested for response biases that might have
contributed to any differences in performance across
the group, thereby possibly confounding the interpretation of the results. Specifically, in Experiment 1, ASD
participants might have exhibited a bias to respond
‘‘counterclockwise’’ when the target appeared on the
left side. In Experiment 2, ASD participants might have
exhibited a bias to report that the lines bisected on the
right when the target appeared on the right. To ensure
that there were no such visual-field, stimulus-response
congruency effects that might have impacted our results
in Experiment 1, we computed a three-way, mixedmodel ANOVA with target side (left, right) and target
tilt (clockwise, counterclockwise) as within-subject
variables and group (ASD, control) as a betweensubjects variable. An analogous ANOVA was computed for Experiment 2 with target-arm eccentricity (T
junction in the outer, inner position) replacing target
tilt. For both experiments, and for both accuracy and
reaction time (RT), we found no main effect of side, no
main effect of tilt/eccentricity, no side · group or tilt/
eccentricity · group interactions, and no side · tilt/
eccentricity · group interactions. In short, there were
no possible stimulus confounds.

Results
Experiment 1: Effects of attention on contrast
sensitivity
We found that exogenous attention modulated
contrast sensitivity during an orientation-discrimination task in the same way and to a statistically
indistinguishable degree in both groups (Figure 2).
Accuracy, RT, and inverse efficiency measures (RT
divided by accuracy, e.g., Kimchi & Peterson, 2008)
were each analyzed with a two-way mixed-model
ANOVA with cue (valid, neutral, invalid) as a withinsubjects variable and group (ASD, control) as a
between-subjects variable (Table 2). There was a main
effect of cue for accuracy (Figure 2A), RT (Figure 2C),
and inverse efficiency. Accuracy was higher, RT was
faster, and performance was more efficient for the valid
than the neutral cue (two-tailed, paired t tests, ps ,
0.05), which, in turn, was higher, faster, and more
efficient than for the invalid cue (two-tailed, paired t
tests, ps , 0.005). There was no main effect of group

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

Figure 2. Experiment 1: contrast sensitivity. (A) Accuracy in the
orientation-discrimination task. Black bars, valid trials. Gray
bars, neutral trials. White bars, invalid trials. ***, significant
main effect of attention ( p , 0.001). Error bars, standard error
of the mean across participants. (B) Individual accuracy data. xaxis, valid trials. y-axis, invalid trials. White circles, ASD group.
Black circles, control group. Black line, unity line. (C) Reaction
time. Same format as panel A. *, significant main effect of group
(p , 0.05). Same format as panel A. (D) Individual reaction
times. Same format as panel B.

for accuracy or efficiency, indicating that overall
proportion correct and overall efficiency did not differ
between the groups. There was, however, a main effect
of group in RT with the ASD group exhibiting
significantly faster RTs across all attention conditions.
There were no cue · group interactions for accuracy,
RT, or efficiency. This means that the magnitudes of
the cueing effects were indistinguishable between the

7

groups, providing evidence that peripheral cues modulated accuracy, RT, and efficiency in the same way
and to a statistically indistinguishable degree in ASD as
in typical development. The pattern of results was
consistent across participants (Figure 2B, D).
Potential group differences in task difficulty and/or
missed responses did not confound these results.
Orientation-discrimination thresholds were determined
in pretests to equate task difficulty separately for each
participant. The amount of tilt (angular degrees from
vertical) at which participants performed the task was
indistinguishable between the groups (ASD: mean ¼
238; range ¼ 2.58–408; control: mean ¼ 178; range ¼ 2.28–
378; t test, p ¼ 0.31), ruling out the possibility that
unequal task difficulty might have masked real
differences in task performance. Additionally, participants were instructed to be as accurate as possible but
to still respond within the 1000 ms response window.
Missed responses were treated as incorrect, but the
proportions of missed responses were low and statistically indistinguishable across groups (ASD: 0.027,
control: 0.028; t test, p ¼ 0.96). Repeating the ANOVAs
with these trials excluded, rather than counted as
incorrect responses, did not qualitatively change the
outcome, and the results supported the same conclusions.

Experiment 2: Effects of attention on crowding
In Experiment 2, we found that exogenous attention
modulated the critical distance during a visual crowding task in the same way and to a statistically
indistinguishable degree in both groups (Figure 3). We
fit exponential functions to model the effect of targetflanker distance on accuracy. Critical distance values
(i.e., the target-flanker distance at which accuracy
reached 90% of the asymptotic value, see Materials and
methods) were subjected to a two-way mixed-model
ANOVA with cue (valid, neutral) as a within-subjects

Cue
Exp 1: accuracy
Exp 1: reaction time
Exp 1: efficiency
Exp 2: critical distance
Exp 2: reaction time
Exp 3: y-intercept
Exp 3: slope
Exp 3: reaction time
Visual search and crowding correlation

F(2, 18)
F(2, 18)
F(2, 18)
F(1, 14)
F(1, 14)
F(1, 16)
F(1, 16)
F(1, 16)
F(1, 16)

¼
¼
¼
¼

13.16,
19.10,
21.59,
18.01,

p , 0.001
p , 0.001
p , 0.001
p , 0.001
¼ 1.71, p ¼ 0.21
¼ 25.17, p , 0.001
¼ 13.66, p , 0.005
¼ 15.76, p , 0.005
¼ 0.001, p ¼ 0.98

Group
F(1, 18)
F(1, 18)
F(1, 18)
F(1, 14)
F(1, 14)
F(1, 16)
F(1, 16)
F(1, 16)
F(1, 16)

¼ 1.30, p ¼ 0.27
¼ 4.77, p , 0.05
¼ 2.91, p ¼ 0.11
¼ 0.01, p ¼ 0.93
¼ 0.95, p ¼ 0.35
¼ 0.01, p ¼ 0.93
¼ 1.94, p ¼ 0.18
¼ 1.46, p ¼ 0.24
¼ 1.26, p ¼ 0.73

Cue · group interaction
F(2,
F(2,
F(2,
F(1,
F(1,
F(1,
F(1,
F(1,
F(1,

36)
36)
36)
14)
14)
16)
16)
16)
16)

¼
¼
¼
¼
¼
¼
¼
¼
¼

0.23,
0.72,
0.56,
0.99,
1.81,
0.31,
0.26,
1.03,
0.60,

p
p
p
p
p
p
p
p
p

¼
¼
¼
¼
¼
¼
¼
¼
¼

0.79
0.50
0.58
0.34
0.20
0.58
0.61
0.39
0.45

Table 2. Experiments 1, 2, and 3 statistics. Notes: F statistics and p values for two-way mixed-model ANOVAs with cue (Experiment 1:
valid, neutral, invalid; Experiments 2 and 3: valid, neutral) as a within-subjects factor and group (ASD, control) as a between-subjects
factor. Italics, statistically significant differences.

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

Figure 3. Experiment 2: crowding. (A) Target-flanker critical
distance. Black bars, valid trials. Gray bars, neutral trials. ***,
significant main effect of attention ( p , 0.001). Error bars,
standard error of the mean across participants. (B) Individual
critical distances. x-axis, neutral trials. y-axis, valid trials. White
circles, ASD group. Black circles, control group. Black line, unity
line.

variable and group (ASD, control) as a betweensubjects variable (Table 2). There was a main effect of
cue, indicating a significant reduction in the critical
distance in valid attention trials compared with neutral
trials (Figure 3A). There was no main effect of group,
meaning that crowding occurred in ‘‘integration
windows’’ that were indistinguishable in size between
groups (Pelli & Tillman, 2008). Finally, there was no
cue · group interaction, indicating that valid peripheral cues reduced the critical distance in both groups
and that this change in critical distance was statistically
indistinguishable between groups. The pattern of
results was consistent across participants (Figure 3B).
Potential group differences in model-fit quality or
estimated parameters did not obscure true differences
between groups. Two-way mixed-model ANOVAs
were conducted on each of the exponential model
parameters separately (x-intercept, scaling factor, and
asymptote), and there were no significant group
differences or significant cue · group interactions
resulting from any of these analyses. The exponential
function fit the data well, and the goodness of fit was
not significantly different between the groups (mean R2
values: ASD ¼ 0.924; control ¼ 0.919; randomization
test, p ¼ 0.81).
There was no evidence for differences in RT between
groups. Median RTs (correct trials only) were subjected
to a three-way mixed-model ANOVA with cue (valid,
neutral) and target-flanker distance as within-subjects
variables and group as a between-subjects variable.
There were no significant interactions between any of
these factors and no main effect of group. After
collapsing across target-flanker distances and cue
conditions, there was still no evidence of a difference in
RT between the groups (t test, p ¼ 0.35).

8

Figure 4. Experiment 3: visual search. Accuracy in visual search
as a function of set size. Open circles and dashed lines, ASD
group. Closed circles and solid lines, control group. Black, valid
trials. Gray, neutral trials. Error bars, standard error of the mean
across participants.

Experiment 3: Effects of attention on visual
search
In Experiment 3, we found that exogenous attention
modulated target discriminability during a visual
search task in the same way and to a statistically
indistinguishable degree in both groups (Figures 4 and
5). We fit straight lines to accuracy as a function of set
size and then submitted slope and y-intercept values to
two-way mixed-model ANOVAs with cue (valid,
neutral) as a within-subjects variable and group (ASD,
control) as a between-subjects variable (statistics in
Table 2). There was a main effect of cue on both
parameters (Figures 4, 5A, C), indicating that exogenous attention significantly decreased the impact of
additional distractor elements (flattened slope) and
improved overall performance (y-intercept). There was
no main effect of group, meaning that both the impact
of additional distractor elements (slope) and the overall
performance (y-intercept) were indistinguishable between the groups. There was no cue · group
interaction for either analysis, meaning that the effects
of cueing on slope and y-intercept were statistically
indistinguishable between groups. The pattern of
results was consistent across participants (Figure 5B,
D).
Potential group differences in fit quality and/or RTs
did not confound these results. Straight lines fit the
data well, and the goodness of fit was not significantly
different between the groups (mean R2 values: ASD ¼
0.81; control ¼ 0.71; randomization test, p ¼ 0.253).
Median RTs (correct trials only) were subjected to a
three-way mixed-model ANOVA with cue and set size
as within-subjects variables and group as a betweensubjects variable. There were no significant interactions
between any of these factors and no main effect of
group. Further, there was no evidence of a difference in

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

9

crowding task did not differ between groups or between
attention conditions. Taken together, this is evidence
that not only are the effects of crowding consistent
between experiments, but also, and importantly for this
study, that the ability of exogenous spatial attention to
alleviate crowding operates in a statistically indistinguishable manner for both groups in both tasks.

ASD severity and attention effects

Figure 5. Experiment 3: visual search. (A) Slope of the regression
line. Black bars, valid trials. Gray bars, neutral trials. **,
significant main effect of attention ( p , 0.005). Error bars,
standard error of the mean across participants. (B) Individual
slopes. White circles, ASD group. Black circles, control group.
Black line, unity line. (C) Y-intercept of the regression line. Same
format as panel A. ***, significant main effect of attention
(p , 0.001). (D) Individual y-intercepts. Same format as panel B.

RT between the groups after collapsing across cue and
set size conditions (t test, p ¼ 0.24).
For the 16 participants (eight from each group) who
completed both Experiments 2 and 3, performance in
visual search significantly correlated with estimated
performance in crowding to a statistically indistinguishable degree in both groups and in both valid and
neutral trials. Distance to the nearest distractor element
was determined for each of the set sizes in the visual
search experiment, and estimated crowding accuracy
was computed using the exponential fit derived in the
crowding experiment separately for each participant
and separately for each attention condition. With
target-flanker distances equated across experiments,
performance at each set size in visual search was
positively correlated with estimated performance in
crowding in both groups and in both attention
conditions (mean correlation values: ASD neutral,
0.88; ASD valid, 0.72; control neutral, 0.79; control
valid, 0.88; t tests, p-values , 0.005). A two-way mixedmodel ANOVA with cue (valid, neutral) as a withinsubjects variable and group (ASD, control) as a
between-subjects variable revealed no significant interactions and no significant main effects (Table 2). This
indicates that the degree of correlation between visual
search performance and estimated performance in the

ASD symptom severity did not significantly correlate
with the magnitudes of the attention effects. For each
experiment and for each attention effect reported (i.e.,
changes in accuracy and RT, Experiment 1; changes in
critical distance and RT, Experiment 2; changes in
slope, intercept, and RT, Experiment 3), we computed
the correlation between the magnitudes of the attention
effects and ADOS scores across participants in the
ASD group separately for each attention effect and
separately for each of the three ADOS subscores.
Before multiple comparison correction, the change in
RT (valid minus invalid) in Experiment 1 correlated
with stereotyped behavior scores on the ADOS ( p ¼
0.02). But given that we performed 21 tests, the critical
p with a Bonferroni correction would be p , 0.0024,
and the correlation in question does not survive this
correction. Furthermore, we note that the same DRT
versus stereotyped behavior correlation yielded a
nonsignificant correlation in the other direction in
Experiment 3. In sum, there is no evidence for a
correlation between the magnitude of the attention
effect and symptom severity.

Discussion
In three different tasks, we found that exogenous
spatial attention cues evoked robust enhancements in
performance for adults with ASD, statistically indistinguishable from the performance enhancements
observed in a matched control group. In Experiment 1,
attention modulated performance accuracy and RTs
when participants judged the orientation of a grating.
In Experiment 2, attention alleviated crowding in the
visual periphery by decreasing the target-flanker
distance needed to discriminate the orientation of a
letter-like figure. In Experiment 3, attention modulated
performance in visual search by increasing performance
overall and reducing the effect of added distractors.
The magnitudes of these cueing effects were statistically
indistinguishable from a matched control group in all
three experiments. Taken together, these data provide
strong evidence for intact exogenous attention function
in high-functioning adults with ASD.

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

The spatial attention literature in ASD is full of
inconsistent findings (for reviews, see Ames & FletcherWatson, 2010; Fan, 2012; Keehn et al., 2013; Simmons
et al., 2009). Despite this large body of empirical work,
only a handful of previous studies have assessed
exogenous spatial attention function in ASD by pairing
a peripheral precue with a covert discrimination task
(Haist, Adamo, Westerfield, Courchesne, & Townsend,
2005; Iarocci & Burack, 2004; Renner, Grofer, Klinger,
& Klinger, 2006; Robertson, Kravitz, Freyberg, BaronCohen, & Baker, 2013; Townsend, Harris, & Courchesne, 1996, task 2). Inconsistent with our results,
ASD-associated deficits in exogenous attention were
reported in all but one (Iarocci & Burack, 2004) of
these previous studies. However, serious methodological concerns limit the validity and interpretability of
these studies.
Research on speed-accuracy trade-offs and attention
have shown that to interpret accuracy-based cueing
effects, it is necessary to analyze RT to rule out possible
trade-offs (Carrasco, Giordano, & McElree, 2004,
2006; Carrasco & McElree, 2001; Giordano et al.,
2009). Two of the previous studies reporting deficits in
exogenous attention in ASD (Renner et al., 2006;
Townsend et al., 1996, task 2) did not report RT
analyses or results. In the most straightforward
example of a speed-accuracy trade-off, the ASD group
might have sped up on valid trials compared to invalid
trials, which would have lowered accuracy in valid but
not invalid trials, thus resulting in a decrease in the
magnitude of the cueing effect (i.e., performance in
valid minus performance in invalid trials). This smaller
cueing effect could be accounted for by the change in
RT rather than a difference in attention between the
groups. In a similar way, control participants could
have slowed down on valid trials compared to invalid
trials, thus artificially inflating the magnitude of their
accuracy-based attention effect. Without comparing
the RT cueing effects between groups, it is impossible
to make a fair comparison of the accuracy-based cuing
effects.
A previous fMRI study reported weaker modulation
of performance accuracy with cue validity in the ASD
group with no group differences or group · attention
interactions in RT (Haist et al., 2005). However, eye
movements were not explicitly monitored during the
task. Instead, the authors inferred eye movements from
the fMRI responses in regions of interest placed over
the eyes of the participants. The inferred eye movements differed between groups, and differences in eye
position during the presentation of the precues, the
target, or both could drastically impact the behavioral
effects reported.
The last relevant study reported a sharper gradient
of spatial attention in adults with ASD compared to
controls (Robertson et al., 2013). The authors used a

10

100% valid precue along the horizontal meridian and
placed the target at one of three distances (near, mid,
and far) above or below the location of the precue.
They found that the change in inverse efficiency scores
(a combined metric of accuracy and RT) from near to
far distances was more drastic for the ASD group than
for the control group. Unfortunately, without a neutral
or invalid cue condition against which to compare the
valid condition, it is impossible to rule out alternative
interpretations. For example, a neutral condition may
have revealed a similar gradient of performance in the
ASD group, and when the change in performance was
computed (i.e., valid versus neutral), it would have been
identical between groups. To conclude anything about
attention, performance in the same task at the same
location must be compared for two or more different
cueing conditions (valid, invalid, neutral) (e.g., Carrasco, 2011). Furthermore, the Robertson et al. study
confounded exogenous and endogenous attention.
Their experiments were performed with three SOAs
between the cue and target. The main result, an
ostensible ‘‘attention gradient effect,’’ was evident only
for the medium and long SOAs and absent for the
shortest SOA. In their long-SOA condition, participants had enough time to allocate endogenous attention to the target location while the stimulus was still
present (e.g., Grubb et al., 2013). Indeed, given that the
cues were always valid (100% predictive of target
location), this would have been the optimal strategy.
The different SOAs, therefore, most likely corresponded to exogenous attention for the shortest SOA,
endogenous attention for the longest SOA, and a
mixture of the two for the medium SOA. Taken
together, is impossible to know if the steeper drop in
performance with cue-target distance in the ASD group
was due to a combination of endogenous and
exogenous attention or to some other nonattentionrelated factor.
We can rule out confounding effects of RT in our
study. In Experiment 1, we measured performance
accuracies and RTs. Despite faster overall RTs in the
ASD group (see below), we showed equivalent attentional modulation of both accuracy and RTs in the
predicted direction in both groups, ruling out any
speed-accuracy trade-offs specific to a particular
attention condition. Further, these results remained
when a combined measure of accuracy and RT was
used (i.e., inverse efficiency). In Experiments 2 and 3,
we introduced a response-delay period (1000 ms) to
minimize differences in RT across participants, and we
found no group differences for RT and no group ·
attention interactions in either of these experiments.
Significantly faster overall RTs for the ASD group in
Experiment 1 should not be interpreted as an attentionrelated difference. It simply means that the ASD group
was responding more quickly than the control group in

Journal of Vision (2013) 13(14):9, 1–13

Grubb et al.

all three attention conditions. The magnitudes of the
changes in RT due to attention (i.e., the RT cueing
effects) were statistically indistinguishable between
groups (interaction p value: 0.50). Ours is not the first
study to show faster overall motor responses in studies
of attention in ASD. Chawarska, Klin, and Volkmar
(2003) found equivalent between-group attention effects for congruent versus incongruent eye gaze cues
but faster overall saccadic latencies for the ASD group.
We found robust effects of crowding in both groups
(Experiment 2), adding to a growing body of inconsistent findings on crowding in ASD. A study in
children with ASD found no group differences in visual
search but did find that adding flankers in a covert
orientation-discrimination task affected the control
group more than the ASD group (Baldassi et al., 2009).
Using centrally presented Landolt-C optotypes, a
second study found no group differences in visual
acuity but did observe that the addition of flankers led
to greater foveal crowding in the control group relative
to the ASD group (Keita, Mottron, & Bertone, 2010;
but see Levi, 2008 for the current debate on whether or
not genuine crowding occurs in the fovea). Such
evidence for reduced crowding in ASD is in conflict
with our conclusions, but the difference in findings
might be due to differences in methodology. Neither of
these studies systematically manipulated target-flanker
distances in the periphery; the former used eight
flankers at a fixed target-flanker distance in the
periphery, and the latter used multiple flanking
distances but for centrally presented (i.e., foveal)
targets. Consistent with our results, a third study used a
QUEST procedure (Watson & Pelli, 1983) to evaluate
critical spacing for target discrimination in the periphery and found no evidence for differences in crowding
between the ASD group and the control group
(Constable, Solomon, Gaigg, & Bowler, 2010). We
extend these results to show that exogenous attention
alleviates crowding and that it does so in both groups in
an indistinguishable manner. That attention reduces
the critical distance in typically developing populations
has been shown previously across a number of
eccentricities (Yeshurun & Rashal, 2010).
We found no evidence for differences between the
groups in visual search (Experiment 3), ostensibly
inconsistent with the extant literature on visual search
superiority in ASD. Compared to matched controls,
children and adults with ASD have been found to be
faster at target detection and less affected by set size in
difficult feature and conjunction search tasks (e.g.,
O’Riordan, 2004; O’Riordan, Plaisted, Driver, &
Baron-Cohen, 2001). In these studies, participants were
allowed to move their eyes, and evidence for superior
performance has been confined to measures of RT.
Consistent with our results, studies that have probed
search superiority using covert, accuracy-based proto-

11

cols have found performance to be indistinguishable
between groups (Baldassi et al., 2009). We speculate,
however, that given the faster RTs observed in
Experiment 1 for the ASD group, faster overall
performance in Experiment 3 (i.e., independent of
attention) might have been observed had we used an
RT-based approach. It is an open empirical question as
to why superior performance should manifest in RT
during overt search but not in accuracy during covert
search. We believe this to be a worthwhile topic for
future research.
Small sample size should not limit the generalizability of these findings. Each experiment used a
psychophysical protocol in which approximately five
participants is typical, yielding large attention effects
that were consistent across individuals and experiments
(Figures 2 through 5). The ANOVAs of the accuracy
data revealed that group · attention interaction F
values were all less than one (indicating that the withingroup variability was larger than the between-group
variability; Table 2). Each experiment was statistically
independent, and they all showed the same pattern of
results: large attention effects that were statistically
indistinguishable between groups. Furthermore, the
RT results from Experiment 1 indicate that we did have
adequate statistical power to detect a between-group
difference. Lastly, for three experiments, the magnitude
of individual attention effects in the ASD group were
not significantly correlated with ASD severity as
indexed by ADOS scores. Given such robust attention
effects and no evidence for heterogeneous performance
across the participants in each group, we take this as
strong evidence against the notion that deficits in
exogenous attention underlie the core ASD-associated
deficits present in high-functioning adults with ASD.
Despite strong assertions that attention is disrupted
in ASD (Fan, 2012; Keehn et al., 2013; Townsend &
Westerfield, 2010), this is still a contentious and hotly
debated issue (Ames & Fletcher-Watson, 2010; Simmons et al., 2009). Our strategy has been to take a
rigorous psychophysics-based approach, using protocols and experimental manipulations that are well
understood in typically developing populations. In a
previous study, we provided evidence for intact control
of voluntary spatial attention in ASD under a variety of
task demands (e.g., manipulation of timing and spatial
spread of attention; Grubb et al., 2013). Here, we have
taken a similar approach to the study of exogenous
spatial attention, which plays a critical role in
modulating early vision, and provide evidence that it
operates reflexively and automatically in high-functioning adults with ASD, just as in typically developing
populations. Taken together, both studies provide
evidence that high-functioning adults with ASD do not
have deficits in covert attention. The functional
integrity of endogenous and exogenous attention in

Journal of Visi